---
title: "Covid-19 Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
vertical_layout: scroll
theme: cerulean
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(leaflet)
library(plotly)
#load("datafinal.Rdata")
source("Data manipulation.R",local = T)
theme_set(ggthemes::theme_economist())
df <- data %>% group_by(location,data_type)%>% summarise(total=sum(value)) %>% pivot_wider(names_from = data_type,values_from=total)%>%arrange(-cases_new)
```
Overview
====================================================================
Sidebar{.sidebar}
---------------------------------------------------------------
Last updated:
```{r}
library(lubridate)
d <- Sys.Date()-1
paste(day(d),months(d),",2020")
```
_About :_
My name is **Md Asrafur Rahman Ashiq**.I am a student of the department of Statistics at University of Dhaka,Bangladesh.I tried to build this dashboard.It's my first dashboard.Any kinds of mistake should be forgiven and mentioned.
Data source: ```coronavirus``` package from Comprehensive R Archive Network(CRAN).
[Know more about me(LinkedIn)]("https://www.linkedin.com/in/md-asrafur-rahman-ashiq-5462ab1a0/")
Row
----------------------------------------------
### Worldwide Confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df$cases_new), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
icon = "fas fa-user-md",
color ="violet")
```
### Death {.value-box}
```{r}
valueBox(value = paste(format(sum(df$deaths_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$deaths_new, na.rm = TRUE) / sum(df$cases_new), 1),
"%)", sep = ""),
caption = "Death Cases",
icon = "fa-heartbeat",
color = "#FF3333")
```
### Recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df$recovered_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$recovered_new, na.rm = TRUE) / sum(df$cases_new), 1),
"%)", sep = ""),
caption = "Recovered Cases",
icon = "fa-walking",
color = "#33FFCC")
```
Row {.tabset}
------------------------------------------------------------------
### Top affected countries
```{r}
p1 <- df %>%filter(location %in% df$location[1:9])%>% ggplot(aes(reorder(location,-cases_new),cases_new))+geom_bar(stat="identity",aes(fill=location))+labs(y="total",x="")+theme(legend.position = "")+scale_y_continuous(breaks = seq(0,4e+6,1000000))+geom_text(aes(label=cases_new),vjust=-10000)+scale_fill_brewer(palette = "Set1")+theme(axis.text.x = element_text(angle = 0))
ggplotly(p1)
```
### Datasets
```{r}
DT::datatable(df)
```
### Countries over date
```{r fig.width=20}
data %>% filter(location==df$location[1:10]) %>% group_by(location,date) %>%
summarise(sum=sum(value))%>% mutate(cum=cumsum(sum)) %>% mutate(lab=ifelse(cum==max(cum),location,""))%>%
ggplot(aes(date,cum))+geom_path(alpha=.2,size=1.2,col="red",aes(group=location))+
ggrepel::geom_text_repel(aes(label=lab),size=7)+scale_x_date(date_breaks = "1 month",date_labels = "%b %d")+theme_bw()
```
Row {.tabset}
------------------------------------------------------------
### Top recovered countries
```{r}
p3 <- df %>%arrange(-recovered_new)%>%filter(location %in% df$location[1:9])%>% ggplot(aes(reorder(location,-recovered_new),recovered_new))+geom_bar(stat="identity",aes(fill=location))+labs(y="total",x="")+theme(legend.position = "")+ geom_text(aes(label=recovered_new),vjust=-10000)+scale_fill_brewer(palette = "Set1")+theme(axis.text.x = element_text(angle = 0))
ggplotly(p3)
```
### Top countries with death
```{r}
p2 <- df %>%filter(location %in% df$location[1:9])%>% ggplot(aes(reorder(location,-deaths_new),deaths_new))+geom_bar(stat="identity",aes(fill=location))+labs(y="total",x="")+theme(legend.position = "")+scale_y_continuous(breaks = seq(0,250000,50000))+geom_text(aes(label=deaths_new),vjust=-10000)+scale_fill_brewer(palette = "Set1")+theme(axis.text.x = element_text(angle = 0))
ggplotly(p2)
```
World Map
================================================================
Sidebar{.sidebar}
---------------------------------------------------------------
Wikipedia says,In December 2019, a pneumonia outbreak was reported in Wuhan, China.On 31 December 2019, the outbreak was traced to a novel strain of coronavirus, which was given the interim name 2019-nCoV by the World Health Organization (WHO), later renamed SARS-CoV-2 by the International Committee on Taxonomy of Viruses.
As of **15 August 2020**, there have been at least 765,029 confirmed deaths and more than 21,173,009 confirmed cases in the COVID-19 pandemic. The Wuhan strain has been identified as a new strain of Betacoronavirus from group 2B with approximately 70% genetic similarity to the SARS-CoV.[114] The virus has a 96% similarity to a bat coronavirus, so it is widely suspected to originate from bats as well. The pandemic has resulted in travel restrictions and nationwide lockdowns in many countries.
Column {data-width=650}
-----------------------------------------------------------------------
### Countries with more than 50k confirmed cases
```{r}
data %>% group_by(location,lat,long,data_type) %>%
summarise(sum=sum(value)) %>% ungroup() %>% filter(data_type=="cases_new",sum>50000) %>% leaflet()%>%addTiles()%>%addProviderTiles("Esri.WorldImagery")%>%setView(0,0,zoom = 1.9)%>%
addMarkers(lat = ~lat,lng = ~long,popup = ~paste(sep="
",
location,"Confirmed Cases :",sum),
label = ~paste(sep="\n",location,"Confirmed Cases :",sum),
labelOptions = labelOptions(textsize = "18px"),
popupOptions = popupOptions(maxHeight = 500))
```
Situation of Bangladesh
===============================================================
Row
----------------------------------------------
### Worldwide Confirmed {.value-box}
```{r}
df <- df %>% filter(location=="Bangladesh")
valueBox(value = paste(format(sum(df$cases_new), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
icon = "fas fa-user-md",
color ="violet")
```
### Death {.value-box}
```{r}
valueBox(value = paste(format(sum(df$deaths_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$deaths_new, na.rm = TRUE) / sum(df$cases_new), 1),
"%)", sep = ""),
caption = "Death Cases",
icon = "fa-heartbeat",
color = "#FF3333")
```
### Recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df$recovered_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$recovered_new, na.rm = TRUE) / sum(df$cases_new), 1),
"%)", sep = ""),
caption = "Recovered Cases",
icon = "fa-walking",
color = "#33FFCC")
```
Column
---------------------------------------------------------------
### Districts with more than 2500 confirmed cases
```{r}
library(sf)
bd <- read_sf("Bangladesh data/BGD_adm2.shp")
district <- readxl::read_xlsx("Bangladesh data/COVID-19.xlsx")
#district <- district %>% mutate_geocode(District,source='dsk')
#save(district,file="lat lon district.Rdata")
load("lat lon district.Rdata")
district <- district %>% mutate(lab=ifelse(Cases>2500,paste(District,Cases),NA))
m <- ggplot(bd)+geom_sf(aes(fill=NAME_2))+
geom_point(data=district,aes(x=lon,y=lat,size=Cases),col="red")+
ggrepel::geom_label_repel(data=district,aes(x=lon,y=lat,label=as.character(lab),alpha=.5))+theme_minimal()+theme(legend.position = "")
m
```
### Bangladesh Overall Scenario
```{r}
data$data_type <- str_replace(data$data_type,"_new","")
p2 <- data %>% filter(location=="Bangladesh") %>%
group_by(data_type,date) %>%
summarise(sum_case=sum(value))%>%
mutate(cum_case=cumsum(sum_case))%>%
ggplot(aes(date,cum_case))+geom_path(aes(col=data_type),size=1.2)+
labs(y="Cumulative case ",x="months of 2020 ",caption = "Data : WHO")+
scale_y_continuous(breaks = seq(0,1.5e+7,50000))+scale_x_date(date_breaks = "1 month",date_labels = "%b %d")
ggplotly(p2)
```
